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data("instacart")

instacart = instacart %>% 
  janitor::clean_names() %>% 
  mutate(
    department = as.factor(department)
  ) %>% 
  select(-user_id, -eval_set ) %>% 
  ungroup()

figure 1

top_dept =  instacart %>% 
  add_count(product_id, sort = TRUE, name = "prod_count_ordered") %>% 
  group_by(department_id) %>% 
  mutate(
    max_item_ordered = max(prod_count_ordered)
  ) %>% 
  filter(max_item_ordered == prod_count_ordered) %>% 
  ungroup() %>% 
  group_by(department_id, order_hour_of_day) %>% 
  add_count(product_id, sort = TRUE, name = "count_time_order") %>% 
  mutate(
    percent_time_order = count_time_order/prod_count_ordered
  ) %>% 
  ggplot(aes(x = order_hour_of_day, y = count_time_order, color = department )) +
  geom_point() + 
  geom_line() +
  labs(title = "Number of most popular item in each department ordered per hour",
       x = "Hour of Day",
       y = "Number ordered")


ggplotly(top_dept)

figure 2

barfig = instacart %>% 
  group_by(order_id) %>% 
  mutate(
    num_items_ordered = max(add_to_cart_order)
  ) %>% 
  ungroup() %>% 
  filter(num_items_ordered == 1) %>% 
  group_by(department_id) %>% 
  add_count(department_id, sort = TRUE, name = "sum_dept_ordered") %>% 
  ungroup() %>% 
  distinct(department, sum_dept_ordered) %>% 
  mutate(
    department = fct_reorder(department, sum_dept_ordered)
  )

plot_ly(barfig, x = ~department, y = ~sum_dept_ordered, color = ~department, type = "bar", colors = "Set1") %>% 
  layout(
    title = 'Number of Department orders with 1 item',
    xaxis = list(title = 'Department',
                 zeroline = TRUE),
    yaxis = list(title = 'Number of orders from department',
                 zeroline = TRUE)
  )

## how does number of items ordered effect % of produce

perc_produced = instacart %>% 
  select(-aisle_id, -aisle, -department_id) %>% 
  group_by(order_id) %>% 
  mutate(
    num_items_ordered = max(add_to_cart_order)
  ) %>%   
  add_count(department, name = "dept_count_ordered") %>% 
  ungroup() %>% 
  filter(department == "produce") %>% 
  distinct(order_id, num_items_ordered, dept_count_ordered,order_dow) %>% 
  rename(num_produce = dept_count_ordered) %>% 
  mutate(
    order_dow = as.factor(order_dow),
    percent_produce = 100*num_produce/num_items_ordered,
    ratio_produce = num_produce/(num_items_ordered - num_produce),
    order_group = case_when(
      num_items_ordered == 2 ~ "2 items",
      between(num_items_ordered, 2, 5) ~ "2-5",
      between(num_items_ordered, 5, 10) ~ "5-10",
      between(num_items_ordered, 10, 20) ~ "10-20",
      between(num_items_ordered, 20, 50) ~ "20-50",
      num_items_ordered > 50 ~ "50+"
    ),
    order_group = fct_relevel(order_group, "2 items", "2-5", "5-10", "10-20", "20-50", "50+")
  ) %>% 
  filter(order_group != "NA")


plot_ly(perc_produced, y = ~percent_produce, color = ~order_group, type = "box", colors = "Set2") %>% 
  layout(
    title = "Distribution of percent produce of carts by size or order",
    xaxis = list(title = 'Number of items in order',
                 zeroline = TRUE),
    
    yaxis = list(title = 'Percent of order made up of produce',
                 zeroline = TRUE)
  )